Dataset statistics
| Number of variables | 18 |
|---|---|
| Number of observations | 1000 |
| Missing cells | 638 |
| Missing cells (%) | 3.5% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 140.8 KiB |
| Average record size in memory | 144.1 B |
Variable types
| CAT | 11 |
|---|---|
| NUM | 7 |
Reproduction
| Analysis started | 2020-07-18 05:01:03.680504 |
|---|---|
| Analysis finished | 2020-07-18 05:02:18.093883 |
| Duration | 1 minute and 14.41 seconds |
| Version | pandas-profiling v2.8.0 |
| Command line | pandas_profiling --config_file config.yaml [YOUR_FILE.csv] |
| Download configuration | config.yaml |
Title has a high cardinality: 999 distinct values | High cardinality |
Director has a high cardinality: 644 distinct values | High cardinality |
Actors has a high cardinality: 996 distinct values | High cardinality |
Actor-1 has a high cardinality: 525 distinct values | High cardinality |
Actor-2 has a high cardinality: 692 distinct values | High cardinality |
Actor-3 has a high cardinality: 788 distinct values | High cardinality |
Actor-4 has a high cardinality: 897 distinct values | High cardinality |
Revenue (Millions) has 128 (12.8%) missing values | Missing |
Metascore has 64 (6.4%) missing values | Missing |
Genre-2 has 105 (10.5%) missing values | Missing |
Genre-3 has 340 (34.0%) missing values | Missing |
Title is uniformly distributed | Uniform |
Director is uniformly distributed | Uniform |
Actors is uniformly distributed | Uniform |
Actor-2 is uniformly distributed | Uniform |
Actor-3 is uniformly distributed | Uniform |
Actor-4 is uniformly distributed | Uniform |
Rank has unique values | Unique |
Description has unique values | Unique |
| Distinct count | 1000 |
|---|---|
| Unique (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 500.5 |
|---|---|
| Minimum | 1 |
| Maximum | 1000 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 50.95 |
| Q1 | 250.75 |
| median | 500.5 |
| Q3 | 750.25 |
| 95-th percentile | 950.05 |
| Maximum | 1000 |
| Range | 999 |
| Interquartile range (IQR) | 499.5 |
Descriptive statistics
| Standard deviation | 288.8194361 |
|---|---|
| Coefficient of variation (CV) | 0.5770618104 |
| Kurtosis | -1.2 |
| Mean | 500.5 |
| Median Absolute Deviation (MAD) | 250 |
| Skewness | 0 |
| Sum | 500500 |
| Variance | 83416.66667 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 1000 | 1 | 0.1% | |
| 329 | 1 | 0.1% | |
| 342 | 1 | 0.1% | |
| 341 | 1 | 0.1% | |
| 340 | 1 | 0.1% | |
| 339 | 1 | 0.1% | |
| 338 | 1 | 0.1% | |
| 337 | 1 | 0.1% | |
| 336 | 1 | 0.1% | |
| 335 | 1 | 0.1% | |
| Other values (990) | 990 | 99.0% |
| Value | Count | Frequency (%) | |
| 1 | 1 | 0.1% | |
| 2 | 1 | 0.1% | |
| 3 | 1 | 0.1% | |
| 4 | 1 | 0.1% | |
| 5 | 1 | 0.1% |
| Value | Count | Frequency (%) | |
| 1000 | 1 | 0.1% | |
| 999 | 1 | 0.1% | |
| 998 | 1 | 0.1% | |
| 997 | 1 | 0.1% | |
| 996 | 1 | 0.1% |
| Distinct count | 999 |
|---|---|
| Unique (%) | 99.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| The Host | 2 |
|---|---|
| Easy A | 1 |
| The Maze Runner | 1 |
| Neighbors 2: Sorority Rising | 1 |
| Alexander and the Terrible, Horrible, No Good, Very Bad Day | 1 |
| Other values (994) |
| Value | Count | Frequency (%) | |
| The Host | 2 | 0.2% | |
| Easy A | 1 | 0.1% | |
| The Maze Runner | 1 | 0.1% | |
| Neighbors 2: Sorority Rising | 1 | 0.1% | |
| Alexander and the Terrible, Horrible, No Good, Very Bad Day | 1 | 0.1% | |
| Project X | 1 | 0.1% | |
| The Thinning | 1 | 0.1% | |
| Pain & Gain | 1 | 0.1% | |
| Dark Places | 1 | 0.1% | |
| Scott Pilgrim vs. the World | 1 | 0.1% | |
| Other values (989) | 989 | 98.9% |
Length
| Max length | 61 |
|---|---|
| Median length | 13 |
| Mean length | 14.539 |
| Min length | 2 |
| Distinct count | 1000 |
|---|---|
| Unique (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| A New York writer on sex and love is finally getting married to her Mr. Big. But her three best girlfriends must console her after one of them inadvertently leads Mr. Big to jilt her. | 1 |
|---|---|
| A dying CIA agent trying to reconnect with his estranged daughter is offered an experimental drug that could save his life in exchange for one last assignment. | 1 |
| When their new next-door neighbors turn out to be a sorority even more debaucherous than the fraternity previously living there, Mac and Kelly team with their former enemy, Teddy, to bring the girls down. | 1 |
| Disgruntled Korean War veteran Walt Kowalski sets out to reform his neighbor, a Hmong teenager who tried to steal Kowalski's prized possession: a 1972 Gran Torino. | 1 |
| Three years after Mike bowed out of the stripper life at the top of his game, he and the remaining Kings of Tampa hit the road to Myrtle Beach to put on one last blow-out performance. | 1 |
| Other values (995) |
| Value | Count | Frequency (%) | |
| A New York writer on sex and love is finally getting married to her Mr. Big. But her three best girlfriends must console her after one of them inadvertently leads Mr. Big to jilt her. | 1 | 0.1% | |
| A dying CIA agent trying to reconnect with his estranged daughter is offered an experimental drug that could save his life in exchange for one last assignment. | 1 | 0.1% | |
| When their new next-door neighbors turn out to be a sorority even more debaucherous than the fraternity previously living there, Mac and Kelly team with their former enemy, Teddy, to bring the girls down. | 1 | 0.1% | |
| Disgruntled Korean War veteran Walt Kowalski sets out to reform his neighbor, a Hmong teenager who tried to steal Kowalski's prized possession: a 1972 Gran Torino. | 1 | 0.1% | |
| Three years after Mike bowed out of the stripper life at the top of his game, he and the remaining Kings of Tampa hit the road to Myrtle Beach to put on one last blow-out performance. | 1 | 0.1% | |
| In the aftermath of a family tragedy, an aspiring author is torn between love for her childhood friend and the temptation of a mysterious outsider. Trying to escape the ghosts of her past, she is swept away to a house that breathes, bleeds - and remembers. | 1 | 0.1% | |
| For three Border Patrol agents working a remote desert checkpoint, the contents of one car will reveal an insidious plot within their own ranks. The next 24 hours will take them on a treacherous journey that could cost them their lives. | 1 | 0.1% | |
| Two sisters decide to throw one last house party before their parents sell their family home. | 1 | 0.1% | |
| Manolo, a young man who is torn between fulfilling the expectations of his family and following his heart, embarks on an adventure that spans three fantastic worlds where he must face his greatest fears. | 1 | 0.1% | |
| An elite military unit comprised of special operatives known as G.I. Joe, operating out of The Pit, takes on an evil organization led by a notorious arms dealer. | 1 | 0.1% | |
| Other values (990) | 990 | 99.0% |
Length
| Max length | 421 |
|---|---|
| Median length | 159 |
| Mean length | 163.232 |
| Min length | 42 |
| Distinct count | 644 |
|---|---|
| Unique (%) | 64.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Ridley Scott | 8 |
|---|---|
| M. Night Shyamalan | 6 |
| Paul W.S. Anderson | 6 |
| David Yates | 6 |
| Michael Bay | 6 |
| Other values (639) |
| Value | Count | Frequency (%) | |
| Ridley Scott | 8 | 0.8% | |
| M. Night Shyamalan | 6 | 0.6% | |
| Paul W.S. Anderson | 6 | 0.6% | |
| David Yates | 6 | 0.6% | |
| Michael Bay | 6 | 0.6% | |
| Christopher Nolan | 5 | 0.5% | |
| Denis Villeneuve | 5 | 0.5% | |
| Martin Scorsese | 5 | 0.5% | |
| Antoine Fuqua | 5 | 0.5% | |
| Woody Allen | 5 | 0.5% | |
| Other values (634) | 943 | 94.3% |
Length
| Max length | 32 |
|---|---|
| Median length | 13 |
| Mean length | 13.139 |
| Min length | 3 |
| Distinct count | 996 |
|---|---|
| Unique (%) | 99.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon | 2 |
|---|---|
| Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett | 2 |
| Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson | 2 |
| Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson | 2 |
| Samuel L. Jackson, Julianna Margulies, Nathan Phillips, Rachel Blanchard | 1 |
| Other values (991) |
| Value | Count | Frequency (%) | |
| Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon | 2 | 0.2% | |
| Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett | 2 | 0.2% | |
| Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson | 2 | 0.2% | |
| Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson | 2 | 0.2% | |
| Samuel L. Jackson, Julianna Margulies, Nathan Phillips, Rachel Blanchard | 1 | 0.1% | |
| Kurt Russell, Zoë Bell, Rosario Dawson, Vanessa Ferlito | 1 | 0.1% | |
| Owen Wilson, Rachel McAdams, Kathy Bates, Kurt Fuller | 1 | 0.1% | |
| Casey Affleck, Chiwetel Ejiofor, Anthony Mackie,Aaron Paul | 1 | 0.1% | |
| Emma Roberts, Dave Franco, Emily Meade, Miles Heizer | 1 | 0.1% | |
| Kristen Stewart, Robert Pattinson, Billy Burke,Sarah Clarke | 1 | 0.1% | |
| Other values (986) | 986 | 98.6% |
Length
| Max length | 77 |
|---|---|
| Median length | 58 |
| Mean length | 58.288 |
| Min length | 43 |
Year
Real number (ℝ≥0)
| Distinct count | 11 |
|---|---|
| Unique (%) | 1.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2012.783 |
|---|---|
| Minimum | 2006 |
| Maximum | 2016 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 2006 |
|---|---|
| 5-th percentile | 2007 |
| Q1 | 2010 |
| median | 2014 |
| Q3 | 2016 |
| 95-th percentile | 2016 |
| Maximum | 2016 |
| Range | 10 |
| Interquartile range (IQR) | 6 |
Descriptive statistics
| Standard deviation | 3.205961508 |
|---|---|
| Coefficient of variation (CV) | 0.00159280037 |
| Kurtosis | -0.8219639755 |
| Mean | 2012.783 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | -0.6898787091 |
| Sum | 2012783 |
| Variance | 10.27818919 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 2016 | 297 | 29.7% | |
| 2015 | 127 | 12.7% | |
| 2014 | 98 | 9.8% | |
| 2013 | 91 | 9.1% | |
| 2012 | 64 | 6.4% | |
| 2011 | 63 | 6.3% | |
| 2010 | 60 | 6.0% | |
| 2007 | 53 | 5.3% | |
| 2008 | 52 | 5.2% | |
| 2009 | 51 | 5.1% |
| Value | Count | Frequency (%) | |
| 2006 | 44 | 4.4% | |
| 2007 | 53 | 5.3% | |
| 2008 | 52 | 5.2% | |
| 2009 | 51 | 5.1% | |
| 2010 | 60 | 6.0% |
| Value | Count | Frequency (%) | |
| 2016 | 297 | 29.7% | |
| 2015 | 127 | 12.7% | |
| 2014 | 98 | 9.8% | |
| 2013 | 91 | 9.1% | |
| 2012 | 64 | 6.4% |
Runtime (Minutes)
Real number (ℝ≥0)
| Distinct count | 94 |
|---|---|
| Unique (%) | 9.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 113.172 |
|---|---|
| Minimum | 66 |
| Maximum | 191 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 66 |
|---|---|
| 5-th percentile | 88 |
| Q1 | 100 |
| median | 111 |
| Q3 | 123 |
| 95-th percentile | 150 |
| Maximum | 191 |
| Range | 125 |
| Interquartile range (IQR) | 23 |
Descriptive statistics
| Standard deviation | 18.81090817 |
|---|---|
| Coefficient of variation (CV) | 0.1662152138 |
| Kurtosis | 0.8583211032 |
| Mean | 113.172 |
| Median Absolute Deviation (MAD) | 12 |
| Skewness | 0.8467127314 |
| Sum | 113172 |
| Variance | 353.8502663 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 108 | 31 | 3.1% | |
| 100 | 28 | 2.8% | |
| 117 | 27 | 2.7% | |
| 110 | 26 | 2.6% | |
| 106 | 26 | 2.6% | |
| 118 | 26 | 2.6% | |
| 102 | 25 | 2.5% | |
| 112 | 24 | 2.4% | |
| 104 | 23 | 2.3% | |
| 123 | 23 | 2.3% | |
| Other values (84) | 741 | 74.1% |
| Value | Count | Frequency (%) | |
| 66 | 1 | 0.1% | |
| 73 | 2 | 0.2% | |
| 80 | 2 | 0.2% | |
| 81 | 5 | 0.5% | |
| 82 | 1 | 0.1% |
| Value | Count | Frequency (%) | |
| 191 | 1 | 0.1% | |
| 187 | 1 | 0.1% | |
| 180 | 3 | 0.3% | |
| 172 | 1 | 0.1% | |
| 170 | 1 | 0.1% |
Rating
Real number (ℝ≥0)
| Distinct count | 55 |
|---|---|
| Unique (%) | 5.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 6.723199999999999 |
|---|---|
| Minimum | 1.9 |
| Maximum | 9.0 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 1.9 |
|---|---|
| 5-th percentile | 5.1 |
| Q1 | 6.2 |
| median | 6.8 |
| Q3 | 7.4 |
| 95-th percentile | 8.1 |
| Maximum | 9 |
| Range | 7.1 |
| Interquartile range (IQR) | 1.2 |
Descriptive statistics
| Standard deviation | 0.9454287893 |
|---|---|
| Coefficient of variation (CV) | 0.1406218451 |
| Kurtosis | 1.322270288 |
| Mean | 6.7232 |
| Median Absolute Deviation (MAD) | 0.6 |
| Skewness | -0.7431419408 |
| Sum | 6723.2 |
| Variance | 0.8938355956 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 7.1 | 52 | 5.2% | |
| 6.7 | 48 | 4.8% | |
| 7 | 46 | 4.6% | |
| 6.3 | 44 | 4.4% | |
| 6.6 | 42 | 4.2% | |
| 7.2 | 42 | 4.2% | |
| 7.3 | 42 | 4.2% | |
| 6.5 | 40 | 4.0% | |
| 7.8 | 40 | 4.0% | |
| 6.2 | 37 | 3.7% | |
| Other values (45) | 567 | 56.7% |
| Value | Count | Frequency (%) | |
| 1.9 | 1 | 0.1% | |
| 2.7 | 2 | 0.2% | |
| 3.2 | 1 | 0.1% | |
| 3.5 | 2 | 0.2% | |
| 3.7 | 2 | 0.2% |
| Value | Count | Frequency (%) | |
| 9 | 1 | 0.1% | |
| 8.8 | 2 | 0.2% | |
| 8.6 | 3 | 0.3% | |
| 8.5 | 6 | 0.6% | |
| 8.4 | 4 | 0.4% |
Votes
Real number (ℝ≥0)
| Distinct count | 997 |
|---|---|
| Unique (%) | 99.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 169808.255 |
|---|---|
| Minimum | 61 |
| Maximum | 1791916 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 61 |
|---|---|
| 5-th percentile | 1260.35 |
| Q1 | 36309 |
| median | 110799 |
| Q3 | 239909.75 |
| 95-th percentile | 526551.85 |
| Maximum | 1791916 |
| Range | 1791855 |
| Interquartile range (IQR) | 203600.75 |
Descriptive statistics
| Standard deviation | 188762.6475 |
|---|---|
| Coefficient of variation (CV) | 1.111622327 |
| Kurtosis | 11.3126809 |
| Mean | 169808.255 |
| Median Absolute Deviation (MAD) | 88402 |
| Skewness | 2.507918483 |
| Sum | 169808255 |
| Variance | 3.56313371e+10 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 1427 | 2 | 0.2% | |
| 97141 | 2 | 0.2% | |
| 291 | 2 | 0.2% | |
| 531112 | 1 | 0.1% | |
| 702 | 1 | 0.1% | |
| 47804 | 1 | 0.1% | |
| 226619 | 1 | 0.1% | |
| 76469 | 1 | 0.1% | |
| 125693 | 1 | 0.1% | |
| 174553 | 1 | 0.1% | |
| Other values (987) | 987 | 98.7% |
| Value | Count | Frequency (%) | |
| 61 | 1 | 0.1% | |
| 96 | 1 | 0.1% | |
| 102 | 1 | 0.1% | |
| 115 | 1 | 0.1% | |
| 164 | 1 | 0.1% |
| Value | Count | Frequency (%) | |
| 1791916 | 1 | 0.1% | |
| 1583625 | 1 | 0.1% | |
| 1222645 | 1 | 0.1% | |
| 1047747 | 1 | 0.1% | |
| 1045588 | 1 | 0.1% |
| Distinct count | 814 |
|---|---|
| Unique (%) | 93.3% |
| Missing | 128 |
| Missing (%) | 12.8% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 82.95637614678898 |
|---|---|
| Minimum | 0.0 |
| Maximum | 936.63 |
| Zeros | 1 |
| Zeros (%) | 0.1% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0.211 |
| Q1 | 13.27 |
| median | 47.985 |
| Q3 | 113.715 |
| 95-th percentile | 293.88 |
| Maximum | 936.63 |
| Range | 936.63 |
| Interquartile range (IQR) | 100.445 |
Descriptive statistics
| Standard deviation | 103.2535405 |
|---|---|
| Coefficient of variation (CV) | 1.244672746 |
| Kurtosis | 10.60763453 |
| Mean | 82.95637615 |
| Median Absolute Deviation (MAD) | 41.285 |
| Skewness | 2.592515866 |
| Sum | 72337.96 |
| Variance | 10661.29362 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 0.03 | 7 | 0.7% | |
| 0.01 | 5 | 0.5% | |
| 0.04 | 4 | 0.4% | |
| 0.02 | 4 | 0.4% | |
| 0.32 | 4 | 0.4% | |
| 0.05 | 4 | 0.4% | |
| 1.29 | 3 | 0.3% | |
| 0.15 | 3 | 0.3% | |
| 2.2 | 3 | 0.3% | |
| 0.54 | 3 | 0.3% | |
| Other values (804) | 832 | 83.2% | |
| (Missing) | 128 | 12.8% |
| Value | Count | Frequency (%) | |
| 0 | 1 | 0.1% | |
| 0.01 | 5 | 0.5% | |
| 0.02 | 4 | 0.4% | |
| 0.03 | 7 | 0.7% | |
| 0.04 | 4 | 0.4% |
| Value | Count | Frequency (%) | |
| 936.63 | 1 | 0.1% | |
| 760.51 | 1 | 0.1% | |
| 652.18 | 1 | 0.1% | |
| 623.28 | 1 | 0.1% | |
| 533.32 | 1 | 0.1% |
| Distinct count | 84 |
|---|---|
| Unique (%) | 9.0% |
| Missing | 64 |
| Missing (%) | 6.4% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 58.98504273504273 |
|---|---|
| Minimum | 11.0 |
| Maximum | 100.0 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Memory size | 7.8 KiB |
Quantile statistics
| Minimum | 11 |
|---|---|
| 5-th percentile | 31 |
| Q1 | 47 |
| median | 59.5 |
| Q3 | 72 |
| 95-th percentile | 85 |
| Maximum | 100 |
| Range | 89 |
| Interquartile range (IQR) | 25 |
Descriptive statistics
| Standard deviation | 17.19475702 |
|---|---|
| Coefficient of variation (CV) | 0.2915104614 |
| Kurtosis | -0.6122051468 |
| Mean | 58.98504274 |
| Median Absolute Deviation (MAD) | 12.5 |
| Skewness | -0.1238873467 |
| Sum | 55210 |
| Variance | 295.6596691 |
Histogram with fixed size bins (bins=10)
| Value | Count | Frequency (%) | |
| 66 | 25 | 2.5% | |
| 72 | 25 | 2.5% | |
| 68 | 25 | 2.5% | |
| 64 | 24 | 2.4% | |
| 57 | 23 | 2.3% | |
| 51 | 22 | 2.2% | |
| 65 | 22 | 2.2% | |
| 48 | 21 | 2.1% | |
| 81 | 21 | 2.1% | |
| 76 | 21 | 2.1% | |
| Other values (74) | 707 | 70.7% | |
| (Missing) | 64 | 6.4% |
| Value | Count | Frequency (%) | |
| 11 | 1 | 0.1% | |
| 15 | 1 | 0.1% | |
| 16 | 1 | 0.1% | |
| 18 | 4 | 0.4% | |
| 19 | 1 | 0.1% |
| Value | Count | Frequency (%) | |
| 100 | 1 | 0.1% | |
| 99 | 1 | 0.1% | |
| 98 | 1 | 0.1% | |
| 96 | 4 | 0.4% | |
| 95 | 3 | 0.3% |
| Distinct count | 525 |
|---|---|
| Unique (%) | 52.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Mark Wahlberg | 11 |
|---|---|
| Christian Bale | 11 |
| Jake Gyllenhaal | 9 |
| Denzel Washington | 9 |
| Leonardo DiCaprio | 9 |
| Other values (520) |
| Value | Count | Frequency (%) | |
| Mark Wahlberg | 11 | 1.1% | |
| Christian Bale | 11 | 1.1% | |
| Jake Gyllenhaal | 9 | 0.9% | |
| Denzel Washington | 9 | 0.9% | |
| Leonardo DiCaprio | 9 | 0.9% | |
| Brad Pitt | 9 | 0.9% | |
| Adam Sandler | 9 | 0.9% | |
| Will Smith | 9 | 0.9% | |
| Tom Hanks | 8 | 0.8% | |
| Daniel Radcliffe | 8 | 0.8% | |
| Other values (515) | 908 | 90.8% |
Length
| Max length | 23 |
|---|---|
| Median length | 13 |
| Mean length | 13.132 |
| Min length | 7 |
| Distinct count | 692 |
|---|---|
| Unique (%) | 69.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Cate Blanchett | 8 |
|---|---|
| Michelle Monaghan | 6 |
| Rose Byrne | 6 |
| Jonah Hill | 5 |
| Robert Pattinson | 5 |
| Other values (687) |
| Value | Count | Frequency (%) | |
| Cate Blanchett | 8 | 0.8% | |
| Michelle Monaghan | 6 | 0.6% | |
| Rose Byrne | 6 | 0.6% | |
| Jonah Hill | 5 | 0.5% | |
| Robert Pattinson | 5 | 0.5% | |
| Kristen Wiig | 5 | 0.5% | |
| Emma Watson | 5 | 0.5% | |
| Hugh Jackman | 5 | 0.5% | |
| Anne Hathaway | 5 | 0.5% | |
| Aaron Eckhart | 4 | 0.4% | |
| Other values (682) | 946 | 94.6% |
Length
| Max length | 27 |
|---|---|
| Median length | 14 |
| Mean length | 14.125 |
| Min length | 6 |
| Distinct count | 788 |
|---|---|
| Unique (%) | 78.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Morgan Freeman | 6 |
|---|---|
| Liam Hemsworth | 5 |
| Mark Ruffalo | 4 |
| Scarlett Johansson | 4 |
| Chiwetel Ejiofor | 4 |
| Other values (783) |
| Value | Count | Frequency (%) | |
| Morgan Freeman | 6 | 0.6% | |
| Liam Hemsworth | 5 | 0.5% | |
| Mark Ruffalo | 4 | 0.4% | |
| Scarlett Johansson | 4 | 0.4% | |
| Chiwetel Ejiofor | 4 | 0.4% | |
| Rupert Grint | 4 | 0.4% | |
| Samuel L. Jackson | 4 | 0.4% | |
| Anna Kendrick | 4 | 0.4% | |
| Willem Dafoe | 4 | 0.4% | |
| Owen Wilson | 4 | 0.4% | |
| Other values (778) | 957 | 95.7% |
Length
| Max length | 26 |
|---|---|
| Median length | 14 |
| Mean length | 14.15 |
| Min length | 7 |
| Distinct count | 897 |
|---|---|
| Unique (%) | 89.8% |
| Missing | 1 |
| Missing (%) | 0.1% |
| Memory size | 7.8 KiB |
| Woody Harrelson | 5 |
|---|---|
| Ralph Fiennes | 4 |
| Mark Strong | 3 |
| Emily Blunt | 3 |
| Ben Kingsley | 3 |
| Other values (892) |
| Value | Count | Frequency (%) | |
| Woody Harrelson | 5 | 0.5% | |
| Ralph Fiennes | 4 | 0.4% | |
| Mark Strong | 3 | 0.3% | |
| Emily Blunt | 3 | 0.3% | |
| Ben Kingsley | 3 | 0.3% | |
| Judi Dench | 3 | 0.3% | |
| Chloë Grace Moretz | 3 | 0.3% | |
| Michael Caine | 3 | 0.3% | |
| Philip Seymour Hoffman | 3 | 0.3% | |
| Stellan Skarsgård | 3 | 0.3% | |
| Other values (887) | 966 | 96.6% |
Length
| Max length | 27 |
|---|---|
| Median length | 13 |
| Mean length | 13.885 |
| Min length | 3 |
Genre-1
Categorical
| Distinct count | 13 |
|---|---|
| Unique (%) | 1.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 7.8 KiB |
| Action | |
|---|---|
| Drama | |
| Comedy | |
| Adventure | |
| Crime | |
| Other values (8) |
| Value | Count | Frequency (%) | |
| Action | 293 | 29.3% | |
| Drama | 195 | 19.5% | |
| Comedy | 175 | 17.5% | |
| Adventure | 75 | 7.5% | |
| Crime | 71 | 7.1% | |
| Biography | 64 | 6.4% | |
| Animation | 49 | 4.9% | |
| Horror | 46 | 4.6% | |
| Mystery | 13 | 1.3% | |
| Thriller | 10 | 1.0% | |
| Other values (3) | 9 | 0.9% |
Length
| Max length | 9 |
|---|---|
| Median length | 6 |
| Mean length | 6.337 |
| Min length | 5 |
| Distinct count | 19 |
|---|---|
| Unique (%) | 2.1% |
| Missing | 105 |
| Missing (%) | 10.5% |
| Memory size | 7.8 KiB |
| Drama | |
|---|---|
| Adventure | |
| Romance | |
| Comedy | |
| Crime | 58 |
| Other values (14) |
| Value | Count | Frequency (%) | |
| Drama | 238 | 23.8% | |
| Adventure | 175 | 17.5% | |
| Romance | 69 | 6.9% | |
| Comedy | 62 | 6.2% | |
| Crime | 58 | 5.8% | |
| Thriller | 52 | 5.2% | |
| Mystery | 49 | 4.9% | |
| Horror | 49 | 4.9% | |
| Fantasy | 35 | 3.5% | |
| Sci-Fi | 28 | 2.8% | |
| Other values (9) | 80 | 8.0% | |
| (Missing) | 105 | 10.5% |
Length
| Max length | 9 |
|---|---|
| Median length | 6 |
| Mean length | 6.202 |
| Min length | 3 |
| Distinct count | 18 |
|---|---|
| Unique (%) | 2.7% |
| Missing | 340 |
| Missing (%) | 34.0% |
| Memory size | 7.8 KiB |
| Thriller | |
|---|---|
| Sci-Fi | |
| Drama | |
| Romance | |
| Fantasy | |
| Other values (13) |
| Value | Count | Frequency (%) | |
| Thriller | 133 | 13.3% | |
| Sci-Fi | 89 | 8.9% | |
| Drama | 80 | 8.0% | |
| Romance | 70 | 7.0% | |
| Fantasy | 62 | 6.2% | |
| Mystery | 44 | 4.4% | |
| Comedy | 42 | 4.2% | |
| Horror | 24 | 2.4% | |
| Family | 24 | 2.4% | |
| History | 21 | 2.1% | |
| Other values (8) | 71 | 7.1% | |
| (Missing) | 340 | 34.0% |
Length
| Max length | 9 |
|---|---|
| Median length | 6 |
| Mean length | 5.336 |
| Min length | 3 |
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.First rows
| Rank | Title | Description | Director | Actors | Year | Runtime (Minutes) | Rating | Votes | Revenue (Millions) | Metascore | Actor-1 | Actor-2 | Actor-3 | Actor-4 | Genre-1 | Genre-2 | Genre-3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Guardians of the Galaxy | A group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe. | James Gunn | Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana | 2014 | 121 | 8.1 | 757074 | 333.13 | 76.0 | Chris Pratt | Vin Diesel | Bradley Cooper | Zoe Saldana | Action | Adventure | Sci-Fi |
| 1 | 2 | Prometheus | Following clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone. | Ridley Scott | Noomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron | 2012 | 124 | 7.0 | 485820 | 126.46 | 65.0 | Noomi Rapace | Logan Marshall-Green | Michael Fassbender | Charlize Theron | Adventure | Mystery | Sci-Fi |
| 2 | 3 | Split | Three girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th. | M. Night Shyamalan | James McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula | 2016 | 117 | 7.3 | 157606 | 138.12 | 62.0 | James McAvoy | Anya Taylor-Joy | Haley Lu Richardson | Jessica Sula | Horror | Thriller | NaN |
| 3 | 4 | Sing | In a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same. | Christophe Lourdelet | Matthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson | 2016 | 108 | 7.2 | 60545 | 270.32 | 59.0 | Matthew McConaughey | Reese Witherspoon | Seth MacFarlane | Scarlett Johansson | Animation | Comedy | Family |
| 4 | 5 | Suicide Squad | A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse. | David Ayer | Will Smith, Jared Leto, Margot Robbie, Viola Davis | 2016 | 123 | 6.2 | 393727 | 325.02 | 40.0 | Will Smith | Jared Leto | Margot Robbie | Viola Davis | Action | Adventure | Fantasy |
| 5 | 6 | The Great Wall | European mercenaries searching for black powder become embroiled in the defense of the Great Wall of China against a horde of monstrous creatures. | Yimou Zhang | Matt Damon, Tian Jing, Willem Dafoe, Andy Lau | 2016 | 103 | 6.1 | 56036 | 45.13 | 42.0 | Matt Damon | Tian Jing | Willem Dafoe | Andy Lau | Action | Adventure | Fantasy |
| 6 | 7 | La La Land | A jazz pianist falls for an aspiring actress in Los Angeles. | Damien Chazelle | Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons | 2016 | 128 | 8.3 | 258682 | 151.06 | 93.0 | Ryan Gosling | Emma Stone | Rosemarie DeWitt | J.K. Simmons | Comedy | Drama | Music |
| 7 | 8 | Mindhorn | A has-been actor best known for playing the title character in the 1980s detective series "Mindhorn" must work with the police when a serial killer says that he will only speak with Detective Mindhorn, whom he believes to be a real person. | Sean Foley | Essie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh | 2016 | 89 | 6.4 | 2490 | NaN | 71.0 | Essie Davis | Andrea Riseborough | Julian Barratt | Kenneth Branagh | Comedy | NaN | NaN |
| 8 | 9 | The Lost City of Z | A true-life drama, centering on British explorer Col. Percival Fawcett, who disappeared while searching for a mysterious city in the Amazon in the 1920s. | James Gray | Charlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland | 2016 | 141 | 7.1 | 7188 | 8.01 | 78.0 | Charlie Hunnam | Robert Pattinson | Sienna Miller | Tom Holland | Action | Adventure | Biography |
| 9 | 10 | Passengers | A spacecraft traveling to a distant colony planet and transporting thousands of people has a malfunction in its sleep chambers. As a result, two passengers are awakened 90 years early. | Morten Tyldum | Jennifer Lawrence, Chris Pratt, Michael Sheen,Laurence Fishburne | 2016 | 116 | 7.0 | 192177 | 100.01 | 41.0 | Jennifer Lawrence | Chris Pratt | Michael Sheen | Laurence Fishburne | Adventure | Drama | Romance |
Last rows
| Rank | Title | Description | Director | Actors | Year | Runtime (Minutes) | Rating | Votes | Revenue (Millions) | Metascore | Actor-1 | Actor-2 | Actor-3 | Actor-4 | Genre-1 | Genre-2 | Genre-3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 990 | 991 | Underworld: Rise of the Lycans | An origins story centered on the centuries-old feud between the race of aristocratic vampires and their onetime slaves, the Lycans. | Patrick Tatopoulos | Rhona Mitra, Michael Sheen, Bill Nighy, Steven Mackintosh | 2009 | 92 | 6.6 | 129708 | 45.80 | 44.0 | Rhona Mitra | Michael Sheen | Bill Nighy | Steven Mackintosh | Action | Adventure | Fantasy |
| 991 | 992 | Taare Zameen Par | An eight-year-old boy is thought to be a lazy trouble-maker, until the new art teacher has the patience and compassion to discover the real problem behind his struggles in school. | Aamir Khan | Darsheel Safary, Aamir Khan, Tanay Chheda, Sachet Engineer | 2007 | 165 | 8.5 | 102697 | 1.20 | 42.0 | Darsheel Safary | Aamir Khan | Tanay Chheda | Sachet Engineer | Drama | Family | Music |
| 992 | 993 | Take Me Home Tonight | Four years after graduation, an awkward high school genius uses his sister's boyfriend's Labor Day party as the perfect opportunity to make his move on his high school crush. | Michael Dowse | Topher Grace, Anna Faris, Dan Fogler, Teresa Palmer | 2011 | 97 | 6.3 | 45419 | 6.92 | NaN | Topher Grace | Anna Faris | Dan Fogler | Teresa Palmer | Comedy | Drama | Romance |
| 993 | 994 | Resident Evil: Afterlife | While still out to destroy the evil Umbrella Corporation, Alice joins a group of survivors living in a prison surrounded by the infected who also want to relocate to the mysterious but supposedly unharmed safe haven known only as Arcadia. | Paul W.S. Anderson | Milla Jovovich, Ali Larter, Wentworth Miller,Kim Coates | 2010 | 97 | 5.9 | 140900 | 60.13 | 37.0 | Milla Jovovich | Ali Larter | Wentworth Miller | Kim Coates | Action | Adventure | Horror |
| 994 | 995 | Project X | 3 high school seniors throw a birthday party to make a name for themselves. As the night progresses, things spiral out of control as word of the party spreads. | Nima Nourizadeh | Thomas Mann, Oliver Cooper, Jonathan Daniel Brown, Dax Flame | 2012 | 88 | 6.7 | 164088 | 54.72 | 48.0 | Thomas Mann | Oliver Cooper | Jonathan Daniel Brown | Dax Flame | Comedy | NaN | NaN |
| 995 | 996 | Secret in Their Eyes | A tight-knit team of rising investigators, along with their supervisor, is suddenly torn apart when they discover that one of their own teenage daughters has been brutally murdered. | Billy Ray | Chiwetel Ejiofor, Nicole Kidman, Julia Roberts, Dean Norris | 2015 | 111 | 6.2 | 27585 | NaN | 45.0 | Chiwetel Ejiofor | Nicole Kidman | Julia Roberts | Dean Norris | Crime | Drama | Mystery |
| 996 | 997 | Hostel: Part II | Three American college students studying abroad are lured to a Slovakian hostel, and discover the grim reality behind it. | Eli Roth | Lauren German, Heather Matarazzo, Bijou Phillips, Roger Bart | 2007 | 94 | 5.5 | 73152 | 17.54 | 46.0 | Lauren German | Heather Matarazzo | Bijou Phillips | Roger Bart | Horror | NaN | NaN |
| 997 | 998 | Step Up 2: The Streets | Romantic sparks occur between two dance students from different backgrounds at the Maryland School of the Arts. | Jon M. Chu | Robert Hoffman, Briana Evigan, Cassie Ventura, Adam G. Sevani | 2008 | 98 | 6.2 | 70699 | 58.01 | 50.0 | Robert Hoffman | Briana Evigan | Cassie Ventura | Adam G. Sevani | Drama | Music | Romance |
| 998 | 999 | Search Party | A pair of friends embark on a mission to reunite their pal with the woman he was going to marry. | Scot Armstrong | Adam Pally, T.J. Miller, Thomas Middleditch,Shannon Woodward | 2014 | 93 | 5.6 | 4881 | NaN | 22.0 | Adam Pally | T.J. Miller | Thomas Middleditch | Shannon Woodward | Adventure | Comedy | NaN |
| 999 | 1000 | Nine Lives | A stuffy businessman finds himself trapped inside the body of his family's cat. | Barry Sonnenfeld | Kevin Spacey, Jennifer Garner, Robbie Amell,Cheryl Hines | 2016 | 87 | 5.3 | 12435 | 19.64 | 11.0 | Kevin Spacey | Jennifer Garner | Robbie Amell | Cheryl Hines | Comedy | Family | Fantasy |